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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> 机器学习入门指南（极简版）</a><ul>
<li class="chapter" data-level="1.1" data-path="index.html"><a href="index.html#python"><i class="fa fa-check"></i><b>1.1</b> Python</a><ul>
<li class="chapter" data-level="1.1.1" data-path="index.html"><a href="index.html#python书"><i class="fa fa-check"></i><b>1.1.1</b> Python——书</a></li>
<li class="chapter" data-level="1.1.2" data-path="index.html"><a href="index.html#python教程"><i class="fa fa-check"></i><b>1.1.2</b> Python——教程</a></li>
<li class="chapter" data-level="1.1.3" data-path="index.html"><a href="index.html#python视频"><i class="fa fa-check"></i><b>1.1.3</b> Python——视频</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="index.html"><a href="index.html#机器学习"><i class="fa fa-check"></i><b>1.2</b> 机器学习</a><ul>
<li class="chapter" data-level="1.2.1" data-path="index.html"><a href="index.html#机器学习书"><i class="fa fa-check"></i><b>1.2.1</b> 机器学习——书</a></li>
<li class="chapter" data-level="1.2.2" data-path="index.html"><a href="index.html#机器学习教程"><i class="fa fa-check"></i><b>1.2.2</b> 机器学习——教程</a></li>
<li class="chapter" data-level="1.2.3" data-path="index.html"><a href="index.html#机器学习视频"><i class="fa fa-check"></i><b>1.2.3</b> 机器学习——视频</a></li>
<li class="chapter" data-level="1.2.4" data-path="index.html"><a href="index.html#机器学习数学基础"><i class="fa fa-check"></i><b>1.2.4</b> 机器学习——数学基础</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="index.html"><a href="index.html#一些经验和建议"><i class="fa fa-check"></i><b>1.3</b> 一些经验和建议</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="python基础.html"><a href="python基础.html"><i class="fa fa-check"></i><b>2</b> Python基础</a><ul>
<li class="chapter" data-level="2.1" data-path="python基础.html"><a href="python基础.html#python-1"><i class="fa fa-check"></i><b>2.1</b> Python</a><ul>
<li class="chapter" data-level="2.1.1" data-path="python基础.html"><a href="python基础.html#python学习教程"><i class="fa fa-check"></i><b>2.1.1</b> Python学习教程</a></li>
<li class="chapter" data-level="2.1.2" data-path="python基础.html"><a href="python基础.html#python学习方法"><i class="fa fa-check"></i><b>2.1.2</b> Python学习方法</a></li>
<li class="chapter" data-level="2.1.3" data-path="python基础.html"><a href="python基础.html#python基础系列"><i class="fa fa-check"></i><b>2.1.3</b> Python基础系列</a></li>
<li class="chapter" data-level="2.1.4" data-path="python基础.html"><a href="python基础.html#python库"><i class="fa fa-check"></i><b>2.1.4</b> Python库</a></li>
</ul></li>
<li class="chapter" data-level="2.2" data-path="python基础.html"><a href="python基础.html#numpy"><i class="fa fa-check"></i><b>2.2</b> Numpy</a></li>
<li class="chapter" data-level="2.3" data-path="python基础.html"><a href="python基础.html#pandas"><i class="fa fa-check"></i><b>2.3</b> Pandas</a></li>
<li class="chapter" data-level="2.4" data-path="python基础.html"><a href="python基础.html#matplotlib"><i class="fa fa-check"></i><b>2.4</b> Matplotlib</a></li>
<li class="chapter" data-level="2.5" data-path="python基础.html"><a href="python基础.html#python数据可视化"><i class="fa fa-check"></i><b>2.5</b> Python数据可视化</a></li>
<li class="chapter" data-level="2.6" data-path="python基础.html"><a href="python基础.html#环境和ide"><i class="fa fa-check"></i><b>2.6</b> 环境和IDE</a><ul>
<li class="chapter" data-level="2.6.1" data-path="python基础.html"><a href="python基础.html#如何选择ide"><i class="fa fa-check"></i><b>2.6.1</b> 如何选择IDE</a></li>
<li class="chapter" data-level="2.6.2" data-path="python基础.html"><a href="python基础.html#pycharm"><i class="fa fa-check"></i><b>2.6.2</b> PyCharm</a></li>
<li class="chapter" data-level="2.6.3" data-path="python基础.html"><a href="python基础.html#vscode"><i class="fa fa-check"></i><b>2.6.3</b> VSCode</a></li>
<li class="chapter" data-level="2.6.4" data-path="python基础.html"><a href="python基础.html#spyderjupyter"><i class="fa fa-check"></i><b>2.6.4</b> Spyder&amp;Jupyter</a></li>
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<li class="chapter" data-level="2.7" data-path="python基础.html"><a href="python基础.html#如何阅读-python-开源项目代码"><i class="fa fa-check"></i><b>2.7</b> 如何阅读 Python 开源项目代码?</a></li>
<li class="chapter" data-level="2.8" data-path="python基础.html"><a href="python基础.html#其他待分类"><i class="fa fa-check"></i><b>2.8</b> 其他（待分类）</a></li>
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<li class="chapter" data-level="3" data-path="数学基础.html"><a href="数学基础.html"><i class="fa fa-check"></i><b>3</b> 数学基础</a><ul>
<li class="chapter" data-level="3.1" data-path="数学基础.html"><a href="数学基础.html#数学学习误区"><i class="fa fa-check"></i><b>3.1</b> 数学学习误区</a></li>
<li class="chapter" data-level="3.2" data-path="数学基础.html"><a href="数学基础.html#机器学习与数学"><i class="fa fa-check"></i><b>3.2</b> 机器学习与数学</a></li>
<li class="chapter" data-level="3.3" data-path="数学基础.html"><a href="数学基础.html#统计学"><i class="fa fa-check"></i><b>3.3</b> 统计学</a></li>
<li class="chapter" data-level="3.4" data-path="数学基础.html"><a href="数学基础.html#概率论"><i class="fa fa-check"></i><b>3.4</b> 概率论</a></li>
<li class="chapter" data-level="3.5" data-path="数学基础.html"><a href="数学基础.html#微积分"><i class="fa fa-check"></i><b>3.5</b> 微积分</a></li>
<li class="chapter" data-level="3.6" data-path="数学基础.html"><a href="数学基础.html#线性代数"><i class="fa fa-check"></i><b>3.6</b> 线性代数</a></li>
<li class="chapter" data-level="3.7" data-path="数学基础.html"><a href="数学基础.html#优化"><i class="fa fa-check"></i><b>3.7</b> 优化</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="机器学习基础.html"><a href="机器学习基础.html"><i class="fa fa-check"></i><b>4</b> 机器学习基础</a><ul>
<li class="chapter" data-level="4.1" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习总览"><i class="fa fa-check"></i><b>4.1</b> 机器学习总览</a></li>
<li class="chapter" data-level="4.2" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习的局限"><i class="fa fa-check"></i><b>4.2</b> 机器学习的局限</a></li>
<li class="chapter" data-level="4.3" data-path="机器学习基础.html"><a href="机器学习基础.html#数据清理和格式化"><i class="fa fa-check"></i><b>4.3</b> 数据清理和格式化</a></li>
<li class="chapter" data-level="4.4" data-path="机器学习基础.html"><a href="机器学习基础.html#探索性数据分析"><i class="fa fa-check"></i><b>4.4</b> 探索性数据分析</a></li>
<li class="chapter" data-level="4.5" data-path="机器学习基础.html"><a href="机器学习基础.html#特征工程和特征选择"><i class="fa fa-check"></i><b>4.5</b> 特征工程和特征选择</a></li>
<li class="chapter" data-level="4.6" data-path="机器学习基础.html"><a href="机器学习基础.html#性能指标"><i class="fa fa-check"></i><b>4.6</b> 性能指标</a></li>
<li class="chapter" data-level="4.7" data-path="机器学习基础.html"><a href="机器学习基础.html#优化方法"><i class="fa fa-check"></i><b>4.7</b> 优化方法</a></li>
<li class="chapter" data-level="4.8" data-path="机器学习基础.html"><a href="机器学习基础.html#超参数调整"><i class="fa fa-check"></i><b>4.8</b> 超参数调整</a></li>
<li class="chapter" data-level="4.9" data-path="机器学习基础.html"><a href="机器学习基础.html#评估最佳模型"><i class="fa fa-check"></i><b>4.9</b> 评估最佳模型</a></li>
<li class="chapter" data-level="4.10" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习资源推荐"><i class="fa fa-check"></i><b>4.10</b> 机器学习资源推荐</a></li>
<li class="chapter" data-level="4.11" data-path="机器学习基础.html"><a href="机器学习基础.html#面试竞赛经验"><i class="fa fa-check"></i><b>4.11</b> 面试&amp;竞赛经验</a></li>
<li class="chapter" data-level="4.12" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习的书怎么读"><i class="fa fa-check"></i><b>4.12</b> 机器学习的书怎么读？</a><ul>
<li class="chapter" data-level="4.12.1" data-path="机器学习基础.html"><a href="机器学习基础.html#统计学习方法"><i class="fa fa-check"></i><b>4.12.1</b> 统计学习方法</a></li>
<li class="chapter" data-level="4.12.2" data-path="机器学习基础.html"><a href="机器学习基础.html#西瓜书"><i class="fa fa-check"></i><b>4.12.2</b> 西瓜书</a></li>
</ul></li>
<li class="chapter" data-level="4.13" data-path="机器学习基础.html"><a href="机器学习基础.html#机器学习工具"><i class="fa fa-check"></i><b>4.13</b> 机器学习工具</a></li>
<li class="chapter" data-level="4.14" data-path="机器学习基础.html"><a href="机器学习基础.html#其他"><i class="fa fa-check"></i><b>4.14</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="机器学习模型.html"><a href="机器学习模型.html"><i class="fa fa-check"></i><b>5</b> 机器学习模型</a><ul>
<li class="chapter" data-level="5.1" data-path="机器学习模型.html"><a href="机器学习模型.html#掌握机器学习算法的三重境界"><i class="fa fa-check"></i><b>5.1</b> 掌握机器学习算法的三重境界</a></li>
<li class="chapter" data-level="5.2" data-path="机器学习模型.html"><a href="机器学习模型.html#天搞定机器学习系统连载中"><i class="fa fa-check"></i><b>5.2</b> 100天搞定机器学习系统（连载中）</a></li>
<li class="chapter" data-level="5.3" data-path="机器学习模型.html"><a href="机器学习模型.html#回归"><i class="fa fa-check"></i><b>5.3</b> 回归</a></li>
<li class="chapter" data-level="5.4" data-path="机器学习模型.html"><a href="机器学习模型.html#逻辑回归"><i class="fa fa-check"></i><b>5.4</b> 逻辑回归</a></li>
<li class="chapter" data-level="5.5" data-path="机器学习模型.html"><a href="机器学习模型.html#决策树"><i class="fa fa-check"></i><b>5.5</b> 决策树</a></li>
<li class="chapter" data-level="5.6" data-path="机器学习模型.html"><a href="机器学习模型.html#主成分分析"><i class="fa fa-check"></i><b>5.6</b> 主成分分析</a></li>
<li class="chapter" data-level="5.7" data-path="机器学习模型.html"><a href="机器学习模型.html#随机森林"><i class="fa fa-check"></i><b>5.7</b> 随机森林</a></li>
<li class="chapter" data-level="5.8" data-path="机器学习模型.html"><a href="机器学习模型.html#xgboost"><i class="fa fa-check"></i><b>5.8</b> XGBoost</a></li>
<li class="chapter" data-level="5.9" data-path="机器学习模型.html"><a href="机器学习模型.html#聚类"><i class="fa fa-check"></i><b>5.9</b> 聚类</a></li>
<li class="chapter" data-level="5.10" data-path="机器学习模型.html"><a href="机器学习模型.html#贝叶斯"><i class="fa fa-check"></i><b>5.10</b> 贝叶斯</a></li>
<li class="chapter" data-level="5.11" data-path="机器学习模型.html"><a href="机器学习模型.html#svm"><i class="fa fa-check"></i><b>5.11</b> SVM</a></li>
<li class="chapter" data-level="5.12" data-path="机器学习模型.html"><a href="机器学习模型.html#降维"><i class="fa fa-check"></i><b>5.12</b> 降维</a></li>
<li class="chapter" data-level="5.13" data-path="机器学习模型.html"><a href="机器学习模型.html#其他-1"><i class="fa fa-check"></i><b>5.13</b> 其他</a></li>
<li class="chapter" data-level="5.14" data-path="机器学习模型.html"><a href="机器学习模型.html#学习方法"><i class="fa fa-check"></i><b>5.14</b> 学习方法</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html"><i class="fa fa-check"></i><b>6</b> 机器学习项目实战</a><ul>
<li class="chapter" data-level="6.1" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#数据分析篇"><i class="fa fa-check"></i><b>6.1</b> 数据分析篇</a></li>
<li class="chapter" data-level="6.2" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#机器学习篇"><i class="fa fa-check"></i><b>6.2</b> 机器学习篇</a></li>
<li class="chapter" data-level="6.3" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#深度学习"><i class="fa fa-check"></i><b>6.3</b> 深度学习</a></li>
<li class="chapter" data-level="6.4" data-path="机器学习项目实战.html"><a href="机器学习项目实战.html#其他-2"><i class="fa fa-check"></i><b>6.4</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="7" data-path="深度学习基础.html"><a href="深度学习基础.html"><i class="fa fa-check"></i><b>7</b> 深度学习基础</a><ul>
<li class="chapter" data-level="7.1" data-path="深度学习基础.html"><a href="深度学习基础.html#入门教程"><i class="fa fa-check"></i><b>7.1</b> 入门教程</a></li>
<li class="chapter" data-level="7.2" data-path="深度学习基础.html"><a href="深度学习基础.html#神经网络"><i class="fa fa-check"></i><b>7.2</b> 神经网络</a></li>
<li class="chapter" data-level="7.3" data-path="深度学习基础.html"><a href="深度学习基础.html#深度学习-1"><i class="fa fa-check"></i><b>7.3</b> 深度学习</a></li>
<li class="chapter" data-level="7.4" data-path="深度学习基础.html"><a href="深度学习基础.html#资源推荐"><i class="fa fa-check"></i><b>7.4</b> 资源推荐</a></li>
<li class="chapter" data-level="7.5" data-path="深度学习基础.html"><a href="深度学习基础.html#其他-3"><i class="fa fa-check"></i><b>7.5</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="8" data-path="工具和框架篇.html"><a href="工具和框架篇.html"><i class="fa fa-check"></i><b>8</b> 工具和框架篇</a><ul>
<li class="chapter" data-level="8.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#常见框架"><i class="fa fa-check"></i><b>8.1</b> 常见框架</a></li>
<li class="chapter" data-level="8.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#sklearn"><i class="fa fa-check"></i><b>8.2</b> sklearn</a><ul>
<li class="chapter" data-level="8.2.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#如何正确地实用sklearn"><i class="fa fa-check"></i><b>8.2.1</b> 如何正确地实用sklearn</a></li>
<li class="chapter" data-level="8.2.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#sklearn入门及技巧篇"><i class="fa fa-check"></i><b>8.2.2</b> sklearn入门及技巧篇</a></li>
</ul></li>
<li class="chapter" data-level="8.3" data-path="工具和框架篇.html"><a href="工具和框架篇.html#tensorflow-vs-pytorch"><i class="fa fa-check"></i><b>8.3</b> TensorFlow VS PyTorch</a><ul>
<li class="chapter" data-level="8.3.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#安装问题"><i class="fa fa-check"></i><b>8.3.1</b> 安装问题</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="工具和框架篇.html"><a href="工具和框架篇.html#tensorflow"><i class="fa fa-check"></i><b>8.4</b> Tensorflow</a></li>
<li class="chapter" data-level="8.5" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch"><i class="fa fa-check"></i><b>8.5</b> Pytorch</a><ul>
<li class="chapter" data-level="8.5.1" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch教程"><i class="fa fa-check"></i><b>8.5.1</b> Pytorch教程</a></li>
<li class="chapter" data-level="8.5.2" data-path="工具和框架篇.html"><a href="工具和框架篇.html#pytorch安装与使用"><i class="fa fa-check"></i><b>8.5.2</b> Pytorch安装与使用</a></li>
</ul></li>
<li class="chapter" data-level="8.6" data-path="工具和框架篇.html"><a href="工具和框架篇.html#其他-4"><i class="fa fa-check"></i><b>8.6</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="9" data-path="开源项目推荐.html"><a href="开源项目推荐.html"><i class="fa fa-check"></i><b>9</b> 开源项目推荐</a></li>
<li class="chapter" data-level="10" data-path="免费资料下载.html"><a href="免费资料下载.html"><i class="fa fa-check"></i><b>10</b> 免费资料下载</a><ul>
<li class="chapter" data-level="10.1" data-path="免费资料下载.html"><a href="免费资料下载.html#python-2"><i class="fa fa-check"></i><b>10.1</b> Python</a></li>
<li class="chapter" data-level="10.2" data-path="免费资料下载.html"><a href="免费资料下载.html#机器学习-1"><i class="fa fa-check"></i><b>10.2</b> 机器学习</a></li>
<li class="chapter" data-level="10.3" data-path="免费资料下载.html"><a href="免费资料下载.html#深度学习-2"><i class="fa fa-check"></i><b>10.3</b> 深度学习</a></li>
<li class="chapter" data-level="10.4" data-path="免费资料下载.html"><a href="免费资料下载.html#其他-5"><i class="fa fa-check"></i><b>10.4</b> 其他</a></li>
<li class="chapter" data-level="10.5" data-path="免费资料下载.html"><a href="免费资料下载.html#数据集"><i class="fa fa-check"></i><b>10.5</b> 数据集</a></li>
<li class="chapter" data-level="10.6" data-path="免费资料下载.html"><a href="免费资料下载.html#r"><i class="fa fa-check"></i><b>10.6</b> R</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="机器学习论文.html"><a href="机器学习论文.html"><i class="fa fa-check"></i><b>11</b> 机器学习论文</a><ul>
<li class="chapter" data-level="11.1" data-path="机器学习论文.html"><a href="机器学习论文.html#如何高效读论文"><i class="fa fa-check"></i><b>11.1</b> 如何高效读论文？</a></li>
<li class="chapter" data-level="11.2" data-path="机器学习论文.html"><a href="机器学习论文.html#机器学习ai必读论文"><i class="fa fa-check"></i><b>11.2</b> 机器学习、AI必读论文</a></li>
<li class="chapter" data-level="11.3" data-path="机器学习论文.html"><a href="机器学习论文.html#深度学习必读论文"><i class="fa fa-check"></i><b>11.3</b> 深度学习必读论文</a></li>
</ul></li>
<li class="chapter" data-level="12" data-path="杂谈.html"><a href="杂谈.html"><i class="fa fa-check"></i><b>12</b> 杂谈</a><ul>
<li class="chapter" data-level="12.1" data-path="杂谈.html"><a href="杂谈.html#数学的故事"><i class="fa fa-check"></i><b>12.1</b> 数学的故事</a></li>
<li class="chapter" data-level="12.2" data-path="杂谈.html"><a href="杂谈.html#统计学-1"><i class="fa fa-check"></i><b>12.2</b> 统计学</a></li>
<li class="chapter" data-level="12.3" data-path="杂谈.html"><a href="杂谈.html#大厂技术观察"><i class="fa fa-check"></i><b>12.3</b> 大厂技术观察</a></li>
<li class="chapter" data-level="12.4" data-path="杂谈.html"><a href="杂谈.html#程序人生"><i class="fa fa-check"></i><b>12.4</b> 程序人生</a></li>
<li class="chapter" data-level="12.5" data-path="杂谈.html"><a href="杂谈.html#效率工具"><i class="fa fa-check"></i><b>12.5</b> 效率工具</a></li>
<li class="chapter" data-level="12.6" data-path="杂谈.html"><a href="杂谈.html#其他-6"><i class="fa fa-check"></i><b>12.6</b> 其他</a></li>
</ul></li>
<li class="chapter" data-level="13" data-path="联系作者.html"><a href="联系作者.html"><i class="fa fa-check"></i><b>13</b> 联系作者</a></li>
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<li><a href="https://bookdown.org" target="blank">本书由 bookdown 强力驱动</a></li>

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          <h1>
            <i class="fa fa-circle-o-notch fa-spin"></i><a href="./">R2ML</a>
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<div id="机器学习模型" class="section level1">
<h1><span class="header-section-number">第 5 章</span> 机器学习模型</h1>
<div id="掌握机器学习算法的三重境界" class="section level2">
<h2><span class="header-section-number">5.1</span> 掌握机器学习算法的三重境界</h2>
<p><strong>完整口述机器学习模型原理</strong></p>
<p>这算是基本操作了，考验逻辑思维和表达能力。</p>
<p><strong>手推机器学习算法原理</strong></p>
<p><img src="./files/手推机器学习算法原理.png"></p>
<p>大家在学习《统计学习方法》或《机器学习》的时候，学完一章，要做到合上书，给你一张白纸，可以把本章算法每一步写的清清楚楚。这里顺便推荐一个我觉得非常不错的机器学习视频教程：<a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931339&amp;idx=1&amp;sn=6ad6da4f380acadc25d8c511364c94f0&amp;chksm=8794ee21b0e367376bd7f2e72fbc89642bc4c524dc57546bbe5f3719f8abd09bea0cbbd69fda&amp;token=2004915986&amp;lang=en_US#rd">shuhuai007大佬的《机器学习-白板推导系列》</a>，大佬用21节课，在白板上一步一步推导算法，讲的非常详细，通俗易懂。</p>
<p><img src="files/image_20220109091703.png"></p>
<p>课程地址：</p>
<p><a href="https://space.bilibili.com/97068901/channel/detail?cid=54167" class="uri">https://space.bilibili.com/97068901/channel/detail?cid=54167</a></p>
<p>有好心的同学将板书做了整理，非常美观，已更新到第19章：</p>
<p><a href="https://github.com/ws13685555932/machine_learning_derivation" class="uri">https://github.com/ws13685555932/machine_learning_derivation</a></p>
<p><strong>机器学习算法Python实现</strong></p>
<p>这就比较考验代码能力了，虽然现在sklearn有现成的包可以调，还是建议大家将常见算法如LR、感知机、k近邻、贝叶斯、SVM、EM、Adaboost、决策树、随机森林、GBDT、XGBoost、聚类等等都试着用Python实现一下。这里推荐三个不错的资源：</p>
<p>作者用python实现了线性回归、逻辑回归、BP神经网络，SVM、K-Mean、PCA、异常检测等算法。</p>
<p><a href="https://github.com/lawlite19/MachineLearning_Python" class="uri">https://github.com/lawlite19/MachineLearning_Python</a></p>
<p>作者将统计学习方法第一版每一章的算法用自己的方式实现一遍，这可是被李航老师点赞的项目！</p>
<p><a href="https://github.com/WenDesi/lihang_book_algorithm" class="uri">https://github.com/WenDesi/lihang_book_algorithm</a></p>
<p>普林斯顿博士后 David Bourgin 最近开源的项目：用 NumPy 手写所有主流 ML 模型，看了一下，代码可读性极强。</p>
<p>项目地址：<a href="https://github.com/ddbourgin/numpy-ml" class="uri">https://github.com/ddbourgin/numpy-ml</a></p>
<p>文档地址：<a href="https://numpy-ml.readthedocs.io/" class="uri">https://numpy-ml.readthedocs.io/</a></p>
</div>
<div id="天搞定机器学习系统连载中" class="section level2">
<h2><span class="header-section-number">5.2</span> 100天搞定机器学习系统（连载中）</h2>
<p>大家好，100天搞定机器学习前54天是对Avik-Jain开源项目100-Days-Of-ML-Code的翻译+自己的理解 <a href="https://github.com/Avik-Jain/100-Days-Of-ML-Code" class="uri">https://github.com/Avik-Jain/100-Days-Of-ML-Code</a> 但是这个项目到54天就鸽掉了，十分可惜。
从第55天开始，我将续写这个栏目。 由于之前的文章太多参考Avik-Jain，我也将不定期对之前的章节进行重置。欢迎star</p>
<p><a href="https://github.com/tjxj/100-Days-Of-ML-Code" class="uri">https://github.com/tjxj/100-Days-Of-ML-Code</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929809&amp;idx=1&amp;sn=6583853472779ddde813391c186e49be&amp;chksm=8794e43bb0e36d2d97bd2200a74e4ef70072afadb3df646131c3bb2522274b87ee3fc049a444&amp;scene=21#wechat_redirect">100天搞定机器学习|Day1数据预处理</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929825&amp;idx=1&amp;sn=87d68cf2b67569905662f5cee6de2412&amp;chksm=8794e40bb0e36d1df52991e60ac433f56135d14799f2d1e638d2159a4f5e54ab7b53a384f080&amp;scene=21#wechat_redirect">100天搞定机器学习|Day2简单线性回归分析</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929849&amp;idx=1&amp;sn=d5662bf397f9621f4afbb271e661927a&amp;chksm=8794e413b0e36d05ea5a9bd40f3a585dcefc0b4c1abddbd562b16c92fabebe237be21548a848&amp;scene=21#wechat_redirect">100天搞定机器学习|Day3多元线性回归</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929868&amp;idx=1&amp;sn=5807d7ddc97088f0322d005c2a611e74&amp;chksm=8794e466b0e36d7027d6dcbe3b536228b97b96f74656dfb62f816495e1933473eddab5c5c1a2&amp;scene=21#wechat_redirect">100天搞定机器学习|Day4-6 逻辑回归</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929908&amp;idx=1&amp;sn=d286fb7b6137cdd38f8da1a442e059f0&amp;chksm=8794e45eb0e36d4827b8a15bb69e80e4a1fea358fccda510e03e078ad5479324b3cf91807173&amp;scene=21#wechat_redirect">100天搞定机器学习|Day7 K-NN</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929948&amp;idx=2&amp;sn=1c2f6263a8ac56b1837e730c2141a474&amp;chksm=8794e4b6b0e36da09a505525b8608736e94f6ea00cd20cee0dc7158291f2338c21411f4bfd39&amp;scene=21#wechat_redirect">100天搞定机器学习|Day8 逻辑回归的数学原理</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929952&amp;idx=1&amp;sn=642aab66caac7bbc7dd781ad9e45c325&amp;chksm=8794e48ab0e36d9c3dc90350d79faf52e50b978a6c0ea99a0b93460f4eae416b4a94ea6de90f&amp;scene=21#wechat_redirect">100天搞定机器学习|Day9-12 支持向量机</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929957&amp;idx=1&amp;sn=b1c4268bb60bde35da5debcbfb4c5f7b&amp;chksm=8794e48fb0e36d99277628ef64c0d1a8e7805cfadd7b389bed2d0eeb202e6366522b4c6e405b&amp;scene=21#wechat_redirect">100天搞定机器学习|Day11 实现KNN</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929966&amp;idx=1&amp;sn=83935cbfd51f56cdb08f1b0444dd2e71&amp;chksm=8794e484b0e36d9226594ac9ba7394d4ac2fd205baef473b4fd6d678fa2cc3e767f32d47ab9b&amp;scene=21#wechat_redirect">100天搞定机器学习|Day13-14 SVM的实现</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930067&amp;idx=1&amp;sn=196f11b78f38b2ebcb2337e126615334&amp;chksm=8794e539b0e36c2fbd97ee475215eb55acdcad85c8c462f495b008eb30b55ed1422f306f82a3&amp;scene=21#wechat_redirect">100天搞定机器学习|Day15 朴素贝叶斯</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930244&amp;idx=1&amp;sn=a2a26eba9293c5c2416ccd17d6676ab5&amp;chksm=8794e5eeb0e36cf8794747a30b8b6cee98db056e9eb62cc075860ed0062b6ef6dc6d5ae81b2e&amp;scene=21#wechat_redirect">100天搞定机器学习|Day16 通过内核技巧实现SVM</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930288&amp;idx=1&amp;sn=9a70bd64ee2033de479ca73f6a118529&amp;chksm=8794e5dab0e36ccc55269d73f810e5dcf8877ee70688e45ecb8fb82b2d48fcd47fe8739a5960&amp;scene=21#wechat_redirect">100天搞定机器学习|Day17-18 神奇的逻辑回归</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930262&amp;idx=1&amp;sn=9d9ba4967c125c3c3fdfa1f852a52663&amp;chksm=8794e5fcb0e36cea0f26491280047d0c8ee164e72ab163553c9e68d8885112aec97c3d418f28&amp;scene=21#wechat_redirect">100天搞定机器学习|Day19-20 加州理工学院公开课：机器学习与数据挖掘</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930417&amp;idx=1&amp;sn=7d95a463c2296ca470b059fde68d7abc&amp;chksm=8794ea5bb0e3634dbd3712d3080cfdc9d2eaca7d3798bc8b22d627cfc3cb4ee4891414c10caf&amp;scene=21#wechat_redirect">100天搞定机器学习|Day21 Beautiful Soup</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930417&amp;idx=2&amp;sn=e5b05e7af739ae3a2df3ad8531bbea51&amp;chksm=8794ea5bb0e3634d5d5e909484d5e61a0ce1dc2a6e7453c18c6f2145b70e810f87ec162d2e39&amp;scene=21#wechat_redirect">100天搞定机器学习|Day22 机器为什么能学习？</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930417&amp;idx=3&amp;sn=d50d29d04b787aafcb48ca742f650deb&amp;chksm=8794ea5bb0e3634d9768b3ed39245ff585bfae895c888ec119fd924bc43dd062e09e6c814bd6&amp;scene=21#wechat_redirect">100天搞定机器学习|Day23-25 决策树及Python实现</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930417&amp;idx=4&amp;sn=0459230c87aef2b3fdc6498971faf205&amp;chksm=8794ea5bb0e3634d8e6b6c58229d81a5782c52df678ccbb894713603341a58c6b8d65b285b0e&amp;scene=21#wechat_redirect">100天搞定机器学习|Day26-29 线性代数的本质</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930417&amp;idx=5&amp;sn=951ffd6b919d03c85a988094cb1edb62&amp;chksm=8794ea5bb0e3634d404a5953e4098a7d214e8c16d6d4a3c7d0f2e712b51d79c164360d4536d4&amp;scene=21#wechat_redirect">100天搞定机器学习|Day 30-32 微积分的本质</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930523&amp;idx=2&amp;sn=2233b72afa4758265e01f0c0aaafe264&amp;chksm=8794eaf1b0e363e7c182dd9edf9f50cb143e8baed7de3c6ea8f40c6b2fbe0085fb0ad965c837&amp;scene=21#wechat_redirect">100天搞定机器学习|Day33-34 随机森林</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931204&amp;idx=2&amp;sn=22ca2eb99fa33aac0ab996cfddaaf667&amp;chksm=8794e9aeb0e360b8a8c5fa22a5ff4a608fa818165dd4a0fb1a227ba9f078575c1ec05a4c6116&amp;scene=21#wechat_redirect">100天搞定机器学习|Day35 深度学习之神经网络的结构</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931235&amp;idx=2&amp;sn=5258d235f155a93a8fecc0d8558a01e4&amp;chksm=8794e989b0e3609fdd82921f218e9c4d4a51316c15e5e38b627bc95171fef230e65391448fc9&amp;scene=21#wechat_redirect">100天搞定机器学习|Day36 深度学习之梯度下降算法</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931273&amp;idx=2&amp;sn=e6d3ba0ab1989daebe6a421b7203368f&amp;chksm=8794e9e3b0e360f50569c807473b10b1f4320020e454a1e7e7340a1301f86efb898be9d87bd9&amp;scene=21&amp;token=123511318&amp;lang=zh_CN#wechat_redirect">100天搞定机器学习|day37 无公式理解反向传播算法之精髓</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931282&amp;idx=2&amp;sn=a343a356a7334e3acb692de8a1b66a86&amp;chksm=8794e9f8b0e360eea16f5024ac82c90f918c4cf71dde65eaea176cc1a51092a2403697474489&amp;scene=21#wechat_redirect">100天搞定机器学习|day38 反向传播算法推导</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931315&amp;idx=2&amp;sn=eb893331ce8bfcecf6a98efd4a1e0811&amp;chksm=8794e9d9b0e360cfe2e2e719369a219921cd017cb27d5ec9d3e79890c97407061ba22cdb48af&amp;scene=21#wechat_redirect">100天搞定机器学习|day39 Tensorflow Keras手写数字识别</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931315&amp;idx=2&amp;sn=eb893331ce8bfcecf6a98efd4a1e0811&amp;chksm=8794e9d9b0e360cfe2e2e719369a219921cd017cb27d5ec9d3e79890c97407061ba22cdb48af&amp;scene=21#wechat_redirect">100天搞定机器学习|day40-42 Tensorflow Keras识别猫狗</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931329&amp;idx=2&amp;sn=c85d0eea79e0a6b631173805bd38f8f2&amp;chksm=8794ee2bb0e3673d1036fcad96205db675d5a1a360ce5de8b18f36e0f87def0caae010139cd4&amp;scene=21#wechat_redirect">100天搞定机器学习|day43 几张GIF理解K-均值聚类原理</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931441&amp;idx=3&amp;sn=609892ad8a7deffb5391beafff0726ae&amp;chksm=8794ee5bb0e3674d00c6576043baec14932f035b9ce3a526c608f5e8412aba2968dac4376a53&amp;scene=21#wechat_redirect">100天搞定机器学习|day44 k均值聚类数学推导与python实现</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931441&amp;idx=4&amp;sn=19b0d91ba8ca7e09eb41ed3306536397&amp;chksm=8794ee5bb0e3674dd5282175be71a7a3e22f9da0515908fd34a1eb22a1474a29bb12119c2e04&amp;scene=21#wechat_redirect">100天搞定机器学习|day45-53 《Python数据科学手册》</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931520&amp;idx=2&amp;sn=968cf54955bd0c56e696618af78a95f4&amp;chksm=8794eeeab0e367fc7539eb55c0183ad5f46420e0d857dccfe6daa205e247b98af6741ad1f5d2&amp;scene=21#wechat_redirect">100天搞定机器学习|day54 聚类系列：层次聚类原理及案例</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648932274&amp;idx=2&amp;sn=5ada70291cab3119e59b26d294f80a25&amp;chksm=8794ed98b0e3648eed6feea95ae55f00fb03110f4b77e29934554f38a0052caafb78ee877365&amp;token=1991487213&amp;lang=zh_CN#rd">100天搞定机器学习|Day55 最大熵模型</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648932334&amp;idx=2&amp;sn=4a99ae273f3f7d1928a6986658ec2fe8&amp;chksm=8794edc4b0e364d21a187a4ce487c2cf5587c648e143d3ec08105943f6e5c49be06b16f075d9&amp;token=1822684797&amp;lang=zh_CN#rd">100天搞定机器学习|Day56 随机森林工作原理及调参实战（信用卡欺诈预测）</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648932515&amp;idx=1&amp;sn=2ac90e1b53169c19140aca36bede4d70&amp;chksm=87941289b0e39b9f493f2e7b5e84ce0d943c3f3185949c0965f61f13991a63373ecc2d772184&amp;token=1141224323&amp;lang=zh_CN#rd">100天搞定机器学习|Day57 Adaboost知识手册(理论篇)</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648935326&amp;idx=1&amp;sn=beccf9c3000cf8e5557fb962c6b29160&amp;chksm=879419b4b0e390a2c6286e07b8c922f78d3265dc70aebfe827b977cd98d03bc159043b49ddad&amp;token=1141224323&amp;lang=zh_CN#rd">100天搞定机器学习|Day58 多分类机器学习中数据不平衡的处理（NSL-KDD 数据集+LightGBM)</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648937158&amp;idx=2&amp;sn=87232a58735a3fc900daf96b7189b49d&amp;chksm=879400ecb0e389fac1922db02b425c708b568ce4e1493ed6df2297c92723af7e5767e20b5026&amp;token=1141224323&amp;lang=zh_CN#rd">100天搞定机器学习|Day59 硬核拆解GBDT</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648948540&amp;idx=1&amp;sn=9b7101248effcf5e00aee7a28edb4383&amp;chksm=87942d16b0e3a400567fc26f81d78c04a74a4ab4c45f06cab5dfd19ac6cb385c874c374b73ad&amp;token=211056560&amp;lang=zh_CN&amp;scene=21#wechat_redirect">100天搞定机器学习|Day60 遇事不决，XGBoost</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648949016&amp;idx=2&amp;sn=e07df5503772e08f96a41845a36c8575&amp;chksm=87945332b0e3da24b507f4b39f1889dae196a699a0b8e92f8fd50d498876f66e0b4803273804&amp;token=211056560&amp;lang=zh_CN&amp;scene=21#wechat_redirect">100天搞定机器学习|Day61 手算+可视化，彻底理解XGBoost</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648934632&amp;idx=3&amp;sn=22fb617d5885be57889250d839221f27&amp;chksm=87941ac2b0e393d451917c328f3204737856a28263f8fe7258dc7b8eff7a38a9f94e580fff9c&amp;scene=21&amp;cur_album_id=1340752070114328576#wechat_redirect">100天搞定机器学习|Day62 随机森林调参实战</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648959975&amp;idx=1&amp;sn=de750b7d2d4c68e2e912dbc131c5195e&amp;chksm=879479cdb0e3f0db52c674044d4cee28d525d370284bf99d327ddb64228701965418abfc073e&amp;token=1786246292&amp;lang=zh_CN#rd">​100天搞定机器学习|Day63 彻底掌握 LightGBM</a></p>
<p><strong>番外：</strong></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648950326&amp;idx=2&amp;sn=5774aa73de4d0558a2d00de39dee7bd6&amp;chksm=8794541cb0e3dd0aad7963dc54cc99b3a677e5bbb317e106d4b0a611fd08df19daf1dec471c0&amp;scene=21#wechat_redirect">100天搞定机器学习：模型训练好了，然后呢？</a></p>
<p><a href="http://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648950329&amp;idx=1&amp;sn=4ed063790e872f149487fac5e5e8b826&amp;chksm=87945413b0e3dd05c2b9f677983a60c8c693fa2faf894894194896912c95dcbf6d50ededf6a6&amp;scene=21#wechat_redirect">100天搞定机器学习：写YAML配置文件</a></p>
</div>
<div id="回归" class="section level2">
<h2><span class="header-section-number">5.3</span> 回归</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931029&amp;idx=1&amp;sn=9e1bae267e5123819d4e50cc0a32e46a&amp;chksm=8794e8ffb0e361e9bd6b2787f9eb1d99ebc9fd106082e3d4dc544ad970349fc049ed9da5be2e&amp;token=2004915986&amp;lang=en_US#rd">常见的七种回归技术</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648944260&amp;idx=2&amp;sn=67d3f7aa0a35800448f8e343949bef46&amp;chksm=87943caeb0e3b5b8bce69250c488ae38b2f6705b277b1e65bdcb89e7f4d55771d15071ebf969&amp;token=281192998&amp;lang=zh_CN#rd">「回归分析」知识点梳理</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929533&amp;idx=1&amp;sn=5f50bbc0bbebb8254a7161417a5cc842&amp;chksm=8794e6d7b0e36fc118605bd15fa1f0adf7528969c27e122d2fae1b29bff430cd300e872fcd30&amp;token=2004915986&amp;lang=en_US#rd">广义线性模型</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648947724&amp;idx=2&amp;sn=dba96966d18d23683df3b0f576cf6911&amp;chksm=87942e26b0e3a730228473ea6b9e2f877def5e16b45e6dc65422bba01218ee3056d5f397fda6&amp;token=281192998&amp;lang=zh_CN#rd">忘掉sklearn，用Python徒手写线性回归</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648930965&amp;idx=1&amp;sn=28093bf9604138c5090959f924267c75&amp;chksm=8794e8bfb0e361a94ac9d3246c81a308ac7d57704db769ee6729653bbd6eec23acbf4cb7565e&amp;token=2004915986&amp;lang=en_US#rd">机器学习算法之岭回归、Lasso回归和ElasticNet回归</a></p>
</div>
<div id="逻辑回归" class="section level2">
<h2><span class="header-section-number">5.4</span> 逻辑回归</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648933860&amp;idx=2&amp;sn=e1c1c3eaf5097db7b7b16dc58b14846d&amp;chksm=879417ceb0e39ed8489a5c87b3747af67cf6a43f99360aaef7ead53921b0724f0c08b20fb35c&amp;token=281192998&amp;lang=zh_CN#rd">逻辑回归模型10问10答</a></p>
</div>
<div id="决策树" class="section level2">
<h2><span class="header-section-number">5.5</span> 决策树</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929760&amp;idx=1&amp;sn=29462c67f3e40f25d1a3568564b4f9af&amp;chksm=8794e7cab0e36edcbf8e672538bbcb2a71b89bba822f25695fd225a5d0e03b2b1f388c067b98#rd">决策树（Decision Tree）ID3算法</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929781&amp;idx=1&amp;sn=16b79e92eb2eb8b336611b68db040d7d&amp;chksm=8794e7dfb0e36ec95a10b9d13953dcfc44a80b7ae6c696d6a209a3195260c3b58743dcf315b9&amp;token=2004915986&amp;lang=en_US#rd">决策树（Decision Tree）C4.5算法</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929784&amp;idx=1&amp;sn=38804009384eac87d4275422f8859a1f&amp;chksm=8794e7d2b0e36ec4fc2941a7e104a64109cb2ca11da86183d766a4774b97b6b5a9e88f531676&amp;token=2004915986&amp;lang=en_US#rd">决策树（Decision Tree）CART算法</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929674&amp;idx=1&amp;sn=493940b84e49321d00cedb9b8a100fe1&amp;chksm=8794e7a0b0e36eb6017a1b61316070f1d1d539a901d64fc731dca54623c549177a149bd76c2e&amp;token=2004915986&amp;lang=en_US#rd">【算法系列】决策树</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929744&amp;idx=1&amp;sn=c5ac7424af801c2dde1630b056bdff93&amp;chksm=8794e7fab0e36eeceaa7f746adac0c3963e5691664b456855da758b46832d16e603a9e642ac4&amp;token=2004915986&amp;lang=en_US#rd">ID3、C4.5、CART三种决策树的区别</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931076&amp;idx=1&amp;sn=d7d0aa69bde1d2a23fefe0dd519edc4b&amp;chksm=8794e92eb0e3603819cc6cc88e7820f3cf926a98fe67719656c4394762d1ef446c9c0c2b0790&amp;token=2004915986&amp;lang=en_US#rd">[最全整理]关于决策树的一切</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648939877&amp;idx=2&amp;sn=d5c2c402969a2a108e5e9914969a30ef&amp;chksm=87940f4fb0e38659438ab2f4768c908cdf7c6969362e90c65c9a0d444c5c11babb906ba24588&amp;token=281192998&amp;lang=zh_CN#rd">机器学习基础：可视化方式理解决策树剪枝</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648941966&amp;idx=2&amp;sn=1b570b4fe7b7d8ccf00c600059fe5e54&amp;chksm=879437a4b0e3beb20335d4967ee70b96c19ab60a3231e90db05796d9f142937b48612ba46ba4&amp;token=281192998&amp;lang=zh_CN#rd">机器学习：不要低估树模型的威力</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648935296&amp;idx=2&amp;sn=cb77cd47a0189804ba9b16ba10415100&amp;chksm=879419aab0e390bcf14a8eb3f56f1ce31750b5d9566263d93269d47e4674bc7600cc1d1636fb&amp;token=281192998&amp;lang=zh_CN#rd">决策树算法的原理（接地气版）</a></p>
</div>
<div id="主成分分析" class="section level2">
<h2><span class="header-section-number">5.6</span> 主成分分析</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929476&amp;idx=1&amp;sn=633bf80ee50c007db430dfae6ea74a9b&amp;chksm=8794e6eeb0e36ff839d0e3e282dd097435307695cf0ebf1bcdc09ad317aca46d60fcd6e743d6&amp;token=2004915986&amp;lang=en_US#rd">【算法系列】主成分分析的数学模型</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929490&amp;idx=1&amp;sn=abf9f68fe49ae17e13440a9e2960f601&amp;chksm=8794e6f8b0e36fee487f9e03a1a3a5b8f2d639efa35263153f7bd1b79bd0f4be2c7bb1f465e3&amp;token=2004915986&amp;lang=en_US#rd">【算法系列】主成分分析的几何意义</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929529&amp;idx=1&amp;sn=37933d662ad7a576cb25de3b154f1ace&amp;chksm=8794e6d3b0e36fc5ecba42b4a6af9e2f3af9ee7661610d3729844146c334ee07d0d3a0469a26&amp;token=2004915986&amp;lang=en_US#rd">【算法系列】主成分分析的推导过程</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648933578&amp;idx=2&amp;sn=e3634eed23230f689ac0d6ddea1cddbf&amp;chksm=879416e0b0e39ff6c9f910bae353405894e0b6386be5b33ece3040b2b483a5064a33193ceffd&amp;token=281192998&amp;lang=zh_CN#rd">换个姿势看马氏距离和主成分分析</a></p>
</div>
<div id="随机森林" class="section level2">
<h2><span class="header-section-number">5.7</span> 随机森林</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929705&amp;idx=1&amp;sn=c3ab83bdb048c595277fe2d1885a2a8c&amp;chksm=8794e783b0e36e956262065dda46cc0626829efb40ef66fcf0e31b34ed9f93b1188f05465502&amp;token=2004915986&amp;lang=en_US#rd">随机森林算法入门(python)</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648952607&amp;idx=2&amp;sn=04a890c9aeae3ec13fc4b4e28f1aecea&amp;chksm=87945d35b0e3d4231accf3c06b077d30c183ff40adaaaad7f827dff7e0ed20314f03c6780e56&amp;token=281192998&amp;lang=zh_CN#rd">随机森林是我最喜欢的模型</a></p>
</div>
<div id="xgboost" class="section level2">
<h2><span class="header-section-number">5.8</span> XGBoost</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648936440&amp;idx=2&amp;sn=f055776b5e62dc0825f6ce960db9def6&amp;chksm=87941dd2b0e394c4637dbf6776acf604abc63985e019a07eaf674a137bf0f54af6de40fdd8a2&amp;token=281192998&amp;lang=zh_CN#rd">XGBoost你真的懂吗？我不信…..</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648936620&amp;idx=2&amp;sn=276e93547ae5e1e24421f50ffd04d99d&amp;chksm=87940286b0e38b90379848646484a68a6d29241c8bfdf43a0d40c2872e51da8f0dc45b297a3b&amp;token=281192998&amp;lang=zh_CN#rd">周志华等人提出sGBM：可微XGBoost算法，性能更强更快！</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648934034&amp;idx=1&amp;sn=a319e4a4fb8ac473ba11c8be9c93f158&amp;chksm=879414b8b0e39dae1afc754b05c1d921941aa412199afb4ddb83d48be49d4e3ca489a6b57b10&amp;token=281192998&amp;lang=zh_CN#rd">GBDT、XGBoost、LightGBM的区别和联系</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648953607&amp;idx=1&amp;sn=02301a64eef2776050d8cc349d8b4fcf&amp;chksm=8794412db0e3c83b1ba317f94f72348c6c5337aeb705c2e1920573dc8ad745a68649f4c58ab3&amp;token=281192998&amp;lang=zh_CN#rd">机器学习：XGBoost vs 神经网络</a></p>
</div>
<div id="聚类" class="section level2">
<h2><span class="header-section-number">5.9</span> 聚类</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648951555&amp;idx=2&amp;sn=599bd28ec02d8a7c961ce1cd8c9a1a11&amp;chksm=87945929b0e3d03f0e1c5895e0f2b3671696e6d6fdd0160e394a4e8db52615f7a9521abeb70c&amp;token=281192998&amp;lang=zh_CN#rd">聚类算法使用小结</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929684&amp;idx=1&amp;sn=7099ebc2af8f0fe3f915934a9d7ac4b4&amp;chksm=8794e7beb0e36ea8740fcf1ed5c4081c945b98cbec109c6f7af65235657b78975c3edc8a041d&amp;token=2004915986&amp;lang=en_US#rd">如何正确使用「K均值聚类」？</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929706&amp;idx=2&amp;sn=caeb2d691717b77c2973836307346ec5&amp;chksm=8794e780b0e36e960013c3b19558d86a1eae1004f2616b25b03a63bc61f54709cb70f6041634&amp;token=2004915986&amp;lang=en_US#rd">四种聚类方法之比较</a></p>
</div>
<div id="贝叶斯" class="section level2">
<h2><span class="header-section-number">5.10</span> 贝叶斯</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648941551&amp;idx=1&amp;sn=6e44e528adcee9389aff68d69db5c6f1&amp;chksm=879431c5b0e3b8d31030d4e56b653765cbab4c8064bb7458038c96d618ead6af2818ae97cca1&amp;token=281192998&amp;lang=zh_CN#rd">贝叶斯：没有人比我更懂南京市长江大桥</a></p>
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<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648948409&amp;idx=2&amp;sn=714174404c9649178126cc5f588864ad&amp;chksm=87942c93b0e3a58529141239566548419ddffd6526df91a693ada65389c1a4e81a13acd01392&amp;token=281192998&amp;lang=zh_CN#rd">实例详解贝叶斯推理的原理</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648949767&amp;idx=3&amp;sn=afe123d9d0cbba66853509f1cafe139f&amp;chksm=8794562db0e3df3bdf7b4d1f7dc4f82f3046cc2224bdf2f6c348dd419e17e8c7fe4393bb47a5&amp;token=281192998&amp;lang=zh_CN#rd">探索贝叶斯定理蕴藏的智慧与哲学</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648949994&amp;idx=1&amp;sn=8b450e14b53680f556cfdac01f7a9732&amp;chksm=879456c0b0e3dfd68de8c39c9493f42872131e4bf7f9ca22d397580e8c4385b5e238b8b83275&amp;token=281192998&amp;lang=zh_CN#rd">GSU 2021 | 贝叶斯数据分析课程开讲</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648953559&amp;idx=2&amp;sn=2d873a580420c8c7e351d4bda10f2ac7&amp;chksm=879440fdb0e3c9ebe5ce9c95b884570fb5ea8a880cfd22a7df589146b154f8aef7d8b4414a34&amp;token=281192998&amp;lang=zh_CN#rd">从贝叶斯定理到概率分布：详解概率论基本定义</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648961440&amp;idx=2&amp;sn=88a47f20a66137f5e49fe3a762df9325&amp;chksm=8794638ab0e3ea9c0224940a56d97552a02c204fd50ed6ed358f2832bdfd2f9b6a384087bf37&amp;token=281192998&amp;lang=zh_CN#rd">【深度好文】Python实现 “贝叶斯” 统计推断！</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648959482&amp;idx=2&amp;sn=9d5e020f9c84fa68847ef7412802cbb3&amp;chksm=87947bd0b0e3f2c6bdeb2e2ee042bef6b7f5c0f322d82666177cc67cc17d88f82c3394bd8db3&amp;token=281192998&amp;lang=zh_CN#rd">【机器学习基础】深入浅出经典贝叶斯统计</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648957253&amp;idx=2&amp;sn=e897add012ea1b08342a64138b4dbc18&amp;chksm=8794736fb0e3fa79366d0eaaac7bedc8f2d21934ae83d338268e6b0f2353cf86909cf86ec6aa&amp;token=281192998&amp;lang=zh_CN#rd">【机器学习基础】分类算法之贝叶斯网络</a></p>
</div>
<div id="svm" class="section level2">
<h2><span class="header-section-number">5.11</span> SVM</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648929573&amp;idx=1&amp;sn=26e57d49ae4ed58bae60a6eafa3429ff&amp;chksm=8794e70fb0e36e19d1524331bdd6493781f1e16c15e72fde08a85ec451de95f5715cf53d44ee&amp;token=2004915986&amp;lang=en_US#rd">SVM的数学推导原理</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648938253&amp;idx=3&amp;sn=f1ddd847ccef4b9cde294edaaecbec1f&amp;chksm=87940527b0e38c3124f744a14a528db340001f3a41c0cdc7385065bce63fd3a985a66c82dc7e&amp;token=281192998&amp;lang=zh_CN#rd">支持向量机背后的数学 -对于SVM背后的数学和理论解释的快速概览及如何实现</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648936351&amp;idx=3&amp;sn=c01368540ac8c561feeb6118ddf0f7ac&amp;chksm=87941db5b0e394a309b99dfac629aaedf8c3cca00dd7f6fa344f0aa933cdc7338a5aed3518d9&amp;token=281192998&amp;lang=zh_CN#rd">我以前一直没有真正理解支持向量机，直到我画了一张图！</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648957035&amp;idx=2&amp;sn=5ce27853b006deecf3e048f3dcc2bb5b&amp;chksm=87947241b0e3fb579bd1c8e41e9ae772d749d39f6047686fc53ee9fffdcee0f41e1229c74505&amp;token=281192998&amp;lang=zh_CN#rd">机器学习基础篇：支持向量机（SVM）理论与实践</a></p>
</div>
<div id="降维" class="section level2">
<h2><span class="header-section-number">5.12</span> 降维</h2>
<p>为什么要进行数据降维?</p>
<p>所谓降维，即用一组个数为 d 的向量 Zi 来代表个数为 D 的向量 Xi 所包含的有用信息，其中 d&lt;D，通俗来讲，即将高维度下降至低维度；将高维数据下降为低维数据。</p>
<p>通常，我们会发现大部分数据集的维度都会高达成百乃至上千，而经典的 MNIST，其维度都是 64。</p>
<p>但在实际应用中，我们所用到的有用信息却并不需要那么高的维度，而且每增加一维所需的样本个数呈指数级增长，这可能会直接带来极大的「维数灾难」;而数据降维就可以实现：</p>
<ul>
<li><p>使得数据集更易使用</p></li>
<li><p>确保变量之间彼此独立</p></li>
<li><p>降低算法计算运算成本</p></li>
</ul>
<p>去除噪音一旦我们能够正确处理这些信息，正确有效地进行降维，这将大大有助于减少计算量，进而提高机器运作效率。而数据降维，也常应用于文本处理、人脸识别、图片识别、自然语言处理等领域。</p>
<p>网上关于各种降维算法的资料参差不齐，同时大部分不提供源代码。这里有个 GitHub 项目整理了使用 Python 实现了 11 种经典的数据抽取(数据降维)算法，包括：PCA、LDA、MDS、LLE、TSNE 等，并附有相关资料、展示效果;非常适合机器学习初学者和刚刚入坑数据挖掘的小伙伴。</p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648932535&amp;idx=2&amp;sn=7333acae1555814271972f159f0b0a2f&amp;chksm=8794129db0e39b8bc3a395995ff4a5f8eabb5cfb628d6174d53d91c5b842428054e48651fd79&amp;token=2004915986&amp;lang=en_US#rd">基于 Python 的 11 种经典数据降维算法</a></p>
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<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648935893&amp;idx=2&amp;sn=3e87b0c790d46f8de91af3790ac9db8b&amp;chksm=87941fffb0e396e9e0d96b67002f9d2241560ce407f08a2d036ba4e8c4a448e6b53b7b6663c2&amp;token=281192998&amp;lang=zh_CN#rd">一文掌握降维算法三剑客 PCA、t-SNE 和自动编码器</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648936914&amp;idx=2&amp;sn=54a0559f0a4abbcae1f98312f9e49cae&amp;chksm=879403f8b0e38aee5e597623e09620ce58a009b969ad87bac02a481e5b41e7aef5118dd87ba5&amp;token=281192998&amp;lang=zh_CN#rd">基于 Python 的 11 种经典数据降维算法</a></p>
</div>
<div id="其他-1" class="section level2">
<h2><span class="header-section-number">5.13</span> 其他</h2>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648961834&amp;idx=3&amp;sn=636c8f6a5573f5204c7eab68bbff01f0&amp;chksm=87946100b0e3e8164f8af5e8e395f67512e4a344c0136b04dd3afe404d4b743a4b4d86b34ae7&amp;token=281192998&amp;lang=zh_CN#rd">马尔科夫决策过程基本概念详解</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931068&amp;idx=3&amp;sn=f0af2f3265bb0b26a132faa87b426e4c&amp;chksm=8794e8d6b0e361c06d1bc3f6807ba1be4749cb90f3f5c3dab9a9fbf80c6a2e2a8b38745a8660&amp;token=2004915986&amp;lang=en_US#rd">各种分类算法的优缺点</a></p>
<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648931068&amp;idx=2&amp;sn=741914361660b2f1b0191bf39fcd794a&amp;chksm=8794e8d6b0e361c0df205671dd69dcaa4ec59eabdf1aeac5ae80046e1d605957e50830fc94db&amp;token=2004915986&amp;lang=en_US#rd">数据分析师最常用的10个机器学习算法！</a></p>
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<div id="学习方法" class="section level2">
<h2><span class="header-section-number">5.14</span> 学习方法</h2>
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<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648933820&amp;idx=1&amp;sn=172cbbdb883ba039e8dd5856c6cdb8ac&amp;chksm=87941796b0e39e809d85feb7b22ef8fdb315d6b7876f93a8846a77eec247a487dec06f4e045b&amp;token=281192998&amp;lang=zh_CN#rd">扒一扒改变世界的十大算法</a></p>
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<p><a href="https://mp.weixin.qq.com/s?__biz=MzA4MjYwMTc5Nw==&amp;mid=2648934085&amp;idx=2&amp;sn=c89ac983a115a4a6e4b104c0ab16c31a&amp;chksm=879414efb0e39df9f6d4fd2a26aa7b22401d490f50d3df3ddeb39712f04bf1f58c4c888c91bb&amp;token=281192998&amp;lang=zh_CN#rd">从小白视角理解『数据挖掘十大算法』</a></p>

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